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Static meshes with texture maps have attracted considerable attention in bothindustrial manufacturing and academic research, leading to an urgentrequirement for effective and robust objective quality evaluation. However,current model-based static mesh quality metrics have obvious limitations: mostof them only consider geometry information, while color information is ignored,and they have strict constraints for the meshes' geometrical topology. Othermetrics, such as image-based and point-based metrics, are easily influenced bythe prepossessing algorithms, e.g., projection and sampling, hampering theirability to perform at their best. In this paper, we propose Geodesic PatchSimilarity (GeodesicPSIM), a novel model-based metric to accurately predicthuman perception quality for static meshes. After selecting a group keypoints,1-hop geodesic patches are constructed based on both the reference anddistorted meshes cleaned by an effective mesh cleaning algorithm. A two-steppatch cropping algorithm and a patch texture mapping module refine the size of1-hop geodesic patches and build the relationship between the mesh geometry andcolor information, resulting in the generation of 1-hop textured geodesicpatches. Three types of features are extracted to quantify the distortion:patch color smoothness, patch discrete mean curvature, and patch pixel coloraverage and variance. To the best of our knowledge, GeodesicPSIM is the firstmodel-based metric especially designed for static meshes with texture maps.GeodesicPSIM provides state-of-the-art performance in comparison withimage-based, point-based, and video-based metrics on a newly created andchallenging database. We also prove the robustness of GeodesicPSIM byintroducing different settings of hyperparameters. Ablation studies alsoexhibit the effectiveness of three proposed features and the patch croppingalgorithm.

GeodesicPSIM: Predicting the Quality of Static Mesh with Texture Map via Geodesic Patch Similarity